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Record W2027367906 · doi:10.1007/s12079-014-0229-7

ALK5 inhibition blocks TGFβ‐induced CCN1 expression in human foreskin fibroblasts

2014· article· en· W2027367906 on OpenAlexafffund
Katherine Thompson, Hannah Murphy‐Marshman, Andrew Leask

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchScleroderma Society of Ontario
KeywordsCTGFForeskinTransforming growth factorCell biologyConnective tissueChemistryGrowth factorFibroblastReceptorCancer researchBiologyCell cultureIn vitroMedicinePathologyBiochemistry

Abstract

fetched live from OpenAlex

The potent profibrotic cytokine TGFβ induces connective tissue growth factor (CCN2/CTGF) is induced in fibroblasts in a fashion sensitive to SB-431542, a specific pharmacological inhibitor of TGFβ type I receptor (ALK5). In several cell types, TGFβ induces CCN1 but suppresses CCN3, which opposes CCN1/CCN2 activities. However, whether SB-431542 alters TGFβ-induced CCN1 or CCN3 in human foreskin fibroblasts in unclear. Here we show that TGFβ induces CCN1 but suppresses CCN3 expression in human foreskin fibroblasts in a SB-431542-sensitive fashion. These results emphasize that CCN1/CCN2 and CCN3 are reciprocally regulated and support the notion that blocking ALK5 or addition of CCN3 may be useful anti-fibrotic approaches.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.291
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2014
Admission routes2
Has abstractyes

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